What Is Quantitative Research? Types, Characteristics & Methods

Ever wondered how companies figure out what we want? The answer lies in numbers. Quantitative research is the art of turning numerical data into valuable insights by uncovering hidden trends and patterns.

From understanding customer behaviors to making smarter business decisions, numbers can tell a powerful story.

In this blog, we’ll explore how this research method can help your business and learn about its types, examples, and expert-backed tips.

What Is Quantitative Research?

Quantitative research is a method that uses numbers and statistics to gather precise, measurable data on the research subject. 

Offering numbers and stats-based insights, this research methodology is a crucial part of primary research and helps understand how well an organizational decision will work out. 

This approach mainly uses online surveys, questionnaires, polls, and quizzes to collect measurable and unbiased data from large groups.

For example, qualitative research can let you know whether your product is a hit or miss, but quantitative data allows you to know exactly what percentage of consumers like it, and what numbers think you still need to improve. As a result of this precision, you can make decisions that are likely to be more in tune with the market demands

Quantitative research thus provides crucial, numbers-based insights that help organizations make informed decisions and predict outcomes effectively.

What Is the Purpose of Quantitative Research?

Quantitative research is the backbone of evidence-based decision-making. Its core purpose is to transform raw data into actionable insights. Researchers can uncover hidden patterns, trends, and relationships that might otherwise go unnoticed by employing statistical methods to analyze numerical information.

Here’s an in-depth analysis of its true objectives:

• Prediction: Quantitative research excels at forecasting future trends or outcomes. By identifying historical patterns, researchers can model potential scenarios and make informed predictions.
• Generalization: By studying a representative sample, researchers can draw conclusions about a larger population. This allows for extrapolating findings to broader groups, providing valuable insights for businesses, policymakers, and scientists.
• Testing Hypotheses: Quantitative research is essential for testing theories and hypotheses. By collecting empirical data, researchers can determine if their assumptions are supported by evidence.
• Measuring Outcomes: Evaluating the effectiveness of interventions or programs is a key application of quantitative research. By tracking specific metrics, researchers can assess the impact of initiatives and make necessary adjustments.
• Understanding Relationships: This research method helps uncover correlations and causal relationships between variables. By identifying how factors influence each other, researchers can develop strategies to optimize outcomes.

What Are the Types and Examples of Quantitative Research?

Quantitative research is usually conducted using two methods. They are-

• Primary quantitative research methods
• Secondary quantitative research methods

Let’s discuss each type in detail.

I. Primary Methods

Primary quantitative research is the most popular method of conducting market research. This method differs from others in that the researcher collects data firsthand instead of using data collected from previous research.

There are multiple types of primary quantitative research. They can be distinguished based on three distinctive aspects: Techniques & types of studies, Data collection methodologies, and Data analysis techniques.

A. Techniques & Types of Studies:

1. Survey Research

Surveys are the easiest, most common, and one of the most sought-after quantitative research techniques. The main aim of a survey is to widely gather and describe the characteristics of a target population or customers. Surveys are the foremost quantitative method preferred by both small and large organizations.

Surveys can be conducted using various methods, such as online polls, web-based surveys, paper questionnaires, phone calls, or face-to-face interviews. Survey research allows organizations to understand customer opinions, preferences, and behavior, making it crucial for market research and decision-making.

You can watch this quick video to learn more about creating surveys. 

Watch: How to Create a Survey Using ProProfs Survey Maker

Surveys, again, are of two types:

2. Cross-Sectional Surveys

Cross-sectional surveys are used to collect data from a sample of the target population at a specific point in time. Researchers evaluate various variables simultaneously to understand the relationships and patterns within the data.

Cross-sectional surveys are popular in retail, small and medium-sized enterprises (SMEs), and healthcare industries, where they assess customer satisfaction, market trends, and product feedback.

3. Longitudinal Surveys

Longitudinal surveys are conducted over an extended period, observing changes in respondent behavior and thought processes. 

Researchers gather data from the same sample multiple times, enabling them to study trends and developments over time. These surveys are valuable in fields such as medicine, applied sciences, and market trend analysis.

4. Correlational Research:

Correlational research aims to establish relationships between two or more variables. 

Researchers use statistical analysis to identify patterns and trends in the data, but it does not determine causality between the variables. This method helps understand how changes in one variable may impact another.

Examples of correlational research questions include studying the relationship between stress and depression, fame and money, or classroom activities and student performance.

5. Causal-Comparative Research:

Causal-comparative research, or quasi-experimental research, seeks to determine cause-and-effect relationships between variables. 

Researchers analyze how an independent variable influences a dependent variable, but they do not manipulate the independent variable. Instead, they observe and compare different groups to conclude.

Causal-comparative research is useful when it’s not ethical or feasible to conduct true experiments. 

Questions for this type of research include analyzing the effect of training programs on employee performance, studying the influence of customer support on client retention, investigating the impact of supply chain efficiency on cost reduction, etc.

6. Experimental Research:

Experimental research is based on testing theories to validate or disprove them. Researchers conduct experiments and manipulate variables to observe their impact on the outcomes.

This type of research is prevalent in natural and social sciences, and it is a powerful method to establish cause-and-effect relationships. By randomly assigning participants to experimental and control groups, researchers can draw more confident conclusions.

Examples of experimental research include studying the effectiveness of a new drug, the impact of teaching methods on student performance, or the outcomes of a marketing campaign.

B. Data Collection Methodologies

After defining research objectives, the next significant step in primary quantitative research is data collection. This involves using two main methods: sampling and conducting surveys or polls. Quantitative research can also be categorized according to their sampling methods.

In quantitative research, there are two primary sampling methods: Probability and Non-probability sampling.

1. Probability Sampling

In probability sampling, researchers use the concept of probability to create samples from a population. This method ensures that every individual in the target audience has an equal chance of being selected for the sample.

There are four main types of probability sampling:

• Simple random sampling: Here, the elements or participants of a sample are selected randomly, and this technique is used in studies that are conducted over considerably large audiences. It works well for large target populations.
• Stratified random sampling: In this method, the entire population is divided into strata or groups, and the sample members get chosen randomly from these strata only. It is always ensured that different segregated strata do not overlap with each other.
• Cluster sampling: Here, researchers divide the population into clusters, often based on geography or demographics. Then, random clusters are selected for the sample.
• Systematic sampling: In this method, only the starting point of the sample is randomly chosen. All the other participants are chosen using a fixed interval. Researchers calculate this interval by dividing the size of the study population by the target sample size.

2. Non-probability Sampling

Non-probability sampling is a method where the researcher’s knowledge and experience guide the selection of samples. This approach doesn’t give all members of the target population an equal chance of being included in the sample.

There are five non-probability sampling models:

• Convenience sampling: The elements or participants are chosen on the basis of their nearness to the researcher. The people in close proximity can be studied and analyzed easily and quickly, as there is no other selection criterion involved. Researchers simply choose samples based on what is most convenient for them. 
• Consecutive sampling: Similar to convenience sampling, researchers select samples one after another over a significant period. They can opt for a single participant or a group of samples to conduct quantitative research in a consecutive manner for a significant period of time. Once this is over, they can conduct the research from the start.
• Quota sampling: With quota sampling, researchers use their understanding of target traits and personalities to form groups (strata). They then choose samples from each stratum based on their own judgment.
• Snowball sampling: This method is used where the target audiences are difficult to contact and interviewed for data collection. Researchers start with a few participants and then ask them to refer others, creating a snowball effect.
• Judgmental sampling: In judgmental sampling, researchers rely solely on their experience and research skills to handpick samples that they believe will be most relevant to the study.

C. Data Analysis Techniques

Once the raw data is collected, the next step in primary quantitative research is data analysis. This crucial process helps draw inferences from the research. It’s essential to connect the results with the objectives and determine their statistical significance.

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To analyze the quantitative data accurately, you’ll need to use specific statistical methods such as:

• SWOT Analysis: This stands for Strengths, Weaknesses, Opportunities, and Threats analysis. Organizations use SWOT analysis to evaluate their performance internally and externally. It helps develop effective improvement strategies.
• Conjoint Analysis: This market research method uncovers how individuals make complex purchasing decisions. It involves considering trade-offs in their daily activities when choosing from a list of product/service options.
• Cross-tabulation: A preliminary statistical market analysis method that reveals relationships, patterns, and trends within various research study parameters.
• TURF Analysis: Short for Totally Unduplicated Reach and Frequency Analysis, this method helps analyze the reach and frequency of favorable communication sources. It provides insights into the potential of a target market.

By using these statistical techniques and inferential statistics methods like confidence intervals and margin of error, you can draw meaningful insights from your primary quantitative research that you can use in making informed decisions.

II. Secondary Methods

Secondary quantitative research, also known as desk research, is a valuable method that uses existing data, called secondary data.

Instead of collecting new data, researchers analyze and combine already available information to enhance their research. This approach involves gathering quantitative data from various sources such as the internet, government databases, libraries, and research reports. 

Secondary quantitative research is crucial in validating data collected through primary quantitative research. It helps reinforce or challenge existing findings.

Here are five commonly used secondary quantitative research methods:

A. Data Available on the Internet:

The Internet has become a vast repository of data, making it easier for researchers to access a wealth of information. Online databases, websites, and research repositories provide valuable quantitative data for researchers to analyze and validate their primary research findings.

B. Government and Non-Government Sources:

Government agencies and non-government organizations often conduct extensive research and publish reports. These reports cover a wide range of topics, providing researchers with reliable and comprehensive data for quantitative analysis.

C. Public Libraries:

While less commonly used in the digital age, public libraries still hold valuable research reports, historical data, and publications that can contribute to quantitative research.

D. Educational Institutions:

Educational institutions frequently conduct research on various subjects. Their research reports and publications can serve as valuable sources of information for researchers, validating and supporting primary quantitative research outcomes.

E. Commercial Information Sources:

Commercial sources such as local newspapers, journals, magazines, and media outlets often publish relevant data on economic trends, market research, and demographic analyses. Researchers can access this data to supplement their own findings and draw better conclusions.

Now, let’s discuss some real-world examples of quantitative research.

Here are two excellent examples of quantitative research methods used by highly distinguished business and consulting organizations. Both examples show how different types of analysis can be performed with qualitative approaches and how the analysis is done once the data is collected.

1. STEP Project Global Consortium / KPMG 2019 Global Family Business Survey

This research utilized quantitative methods to identify ways that kept the family businesses sustainably profitable with time. 

The study also identified the ways in which the family business behavior changed with demographic changes and had “why” and “how” questions. Their qualitative research methods allowed the KPMG team to dig deeper into the mindsets and perspectives of the business owners and uncover unexpected research avenues as well.

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It was a joint effort in which STEP Project Global Consortium collected 26 cases, and KPMG collected 11 cases. 

The research reached the stage of data analysis in 2020, and the analysis process spanned over 4 stages. 

The results, which were also the reasons why family businesses tend to lose their strength with time, were found to be:

• Succession
• Family governance
• Family business legacy

2. EY Seren Teams Research 2020

This is yet another commendable example of qualitative research where the EY Seren Team digs into the unexplored depths of human behavior and how it affected their brand or service expectations. 

The research was done across 200+ sources and involved in-depth virtual interviews with people in their homes, exploring their current needs and wishes. It also involved diary studies across the entire UK customer base to analyze human behavior changes and patterns. 

The study also included interviews with professionals and design leaders from a wide range of industries to explore how COVID-19 transformed their industries. Finally, quantitative surveys were conducted to gain insights into the EY community after every 15 days. 

The insights and results were:

• A culture of fear, daily resilience, and hopes for a better world and a better life – these were the macro trends.
• People felt massive digitization was a resourceful yet demanding aspect as they had to adapt every day.
• Some people wished to have a new world with lots of possibilities, and some were looking for a new purpose.

Advantages of Quantitative Research

Quantitative research data is often standardized and can be easily used to generalize findings for making crucial business decisions and uncover insights to supplement the qualitative research findings. 

Here are some core benefits this research methodology offers.

A. Objectivity and Reliability

• Unbiased Insights: By relying on numerical data, quantitative research minimizes researcher bias, leading to more objective findings.
• Reproducibility: The standardized methods used in quantitative research allow for the replication of studies, enhancing the reliability and credibility of results.

B. Efficiency and Scalability

• Large Sample Sizes: Quantitative methods enable researchers to gather data from large populations, providing a comprehensive understanding of trends and patterns.
• Rapid Data Collection: Efficient data collection techniques, such as online surveys, expedite the research process.
• Cost-Effective: Compared to qualitative research, quantitative methods can be more cost-effective due to their efficiency and scalability.

C. Precision and Generalizability

• Numerical Precision: Quantitative data provides precise measurements and statistical analysis, allowing for accurate comparisons and predictions.
• Generalizability: Findings from large, representative samples can be generalized to the broader population more confidently.

D. Data-Driven Decision-Making

• Informed Choices: Quantitative research offers actionable insights to inform strategic decisions in business, healthcare, policymaking, and other fields.
• Performance Measurement: By tracking key performance indicators (KPIs), organizations can measure the effectiveness of their initiatives and make necessary adjustments.

Disadvantages of Quantitative Research

Despite its numerous benefits, quantitative research has some cons as well, such as:

A. Lack of Depth and Context

Quantitative research excels at providing numerical data but falls short when it comes to understanding the underlying reasons for behaviors or opinions. It offers a snapshot of what happened but not necessarily why it happened. Researchers may struggle to delve into the complexities of human experiences and motivations.

B. Overreliance on Structured Data

The rigid nature of quantitative research limits the exploration of unexpected findings. Researchers are confined to pre-determined questions and response options, potentially overlooking valuable insights from open-ended exploration.

C. Potential for Bias

While striving for objectivity, quantitative research is not immune to bias. Factors such as sample selection, question-wording, and data analysis methods can introduce biases that distort the results. Careful planning and execution are essential to mitigate these risks.

E. Difficulty Establishing Causality

Correlation does not equal causation. While quantitative research can identify relationships between variables, establishing definitive cause-and-effect links is challenging. Other factors may influence the observed relationship, making it difficult to isolate the true cause.

F. Resource Intensive

Conducting quantitative research often requires significant time and financial resources. Collecting large datasets, employing statistical analysis, and ensuring data quality can be costly and time-consuming. Additionally, specialized expertise may be needed for data analysis and interpretation.

Characteristics of Quantitative Research

Quantitative research clarifies the fuzziness of research data from qualitative research analysis. With numerical insights, you can formulate a better and more profitable business decision.

Hence, quantitative research is more readily contestable, sharpens intelligent discussion, helps you see the rival hypotheses, and dynamically contributes to the research process.

Let us have a quick look at some of its characteristics.

1. Measurable Variables

The data collection methods in quantitative research are structured and contain items requiring measurable variables, such as age, number of family members, salary range, highest education, etc.

These structured data collection methods comprise polls, surveys, questionnaires, etc., and may have questions like the ones shown in the following image:

As you can see, all the variables are measurable. This ensures the research is in-depth and provides less erroneous data for reliable, actionable insights.

2. Sample Size

No matter what data analysis methods are used for quantitative research, the sample size is kept small enough to represent the target market.

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The main aim of the research methodology is to obtain numerical insights, so the sample size should be fairly large. Depending on the survey objective and scope, it might include hundreds of thousands of people.

3. Normal Population Distribution

To maintain the reliability of a quantitative research methodology, we assume that the population distribution curve is normal.

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This type of population distribution curve is preferred over a non-normal distribution as the sample size is large, and the characteristics of the sample vary with its size.

This requires adhering to the random sampling principle to avoid the researcher’s bias in interpreting the results. Any bias can ruin the fairness of the entire process and defeat the purpose of the research.

4. Well-Structured Data Representation

Data analysis in quantitative research produces highly structured results and can form well-defined graphical representations. Some common examples include tables, figures, graphs, etc., that combine large blocks of data.

This way, you can discover hidden data trends, relationships, and differences among various measurable variables. This can help researchers understand the survey data and formulate actionable insights for decision-making.

5. Predictable Outcomes

Quantitative data analysis can also be used to estimate and predict outcomes. You can construct if-then scenarios and analyze the data to identify upcoming trends or events.

However, this requires advanced analytics and involves complex mathematical computations. So, it is mostly done via quantitative research tools with advanced analytics capabilities.

When to Use Quantitative Research

Quantitative research is particularly suited for the following scenarios:

1. Testing Hypotheses and Theories

• Hypothesis Verification: When you have a specific prediction about a relationship between variables, quantitative research can be used to test its validity.
• Theory Building: By gathering numerical data, you can identify patterns and trends that contribute to the development of new theories or the refinement of existing ones.

2. Measuring and Comparing

• Market Research: Assessing market size, share, and customer preferences to inform product development or marketing strategies.
• Performance Evaluation: Quantifying the effectiveness of programs, policies, or interventions through metrics and benchmarks.
• Comparative Analysis: Comparing different groups or conditions to identify similarities, differences, and relationships.

3. Generalizing Findings

• Population Representation: When you aim to draw conclusions about a larger population based on a representative sample.
• Predictive Modeling: Building models to forecast future trends or outcomes based on historical data.

4. Identifying Cause-and-Effect Relationships

• Experimental Design: While challenging, quantitative research can be used to establish causal relationships through controlled experiments.
• Correlational Analysis: Identifying patterns and associations between variables, though caution is needed to avoid assuming causation.

In essence, quantitative research is most appropriate when you seek precise, measurable data to answer specific research questions and make informed decisions.

Best Practices for Conducting Quantitative Research

Here are some best practices to keep in mind while conducting quantitative research:

1. Understand Your Research Objectives

There are many ways to collect data via quantitative research methods chosen according to the research objective and scope. These methods allow you to make your own observations regarding any hypotheses—unknown, entirely new, or unexplained. 

Based on that, you can hypothesize proof and build a prediction of outcomes that support the same. You can also create a detailed stepwise plan for data collection, analysis, and testing. 

2. Keep Your Questions Simple

The surveys are meant to reach people en-masse, including a wide demographic range with recipients from all walks of life. Asking simple questions will ensure that they grasp what’s being asked easily.

3. Develop a Solid Research Design

Choose an appropriate research design that aligns with your objectives, whether experimental, quasi-experimental, or correlational. You also need to pay attention to the sample size and sampling technique so that they accurately represent the target population.

4. Use Reliable & Valid Instruments

It’s crucial to select or develop measurement instruments such as questionnaires, scales, or tests that have been validated and are reliable. Before proceeding with the main study, pilot-test these instruments on a small sample to assess their effectiveness and make any necessary improvements.

5. Ensure Data Quality

Double-check data entries and cleaning procedures to eliminate any inconsistencies or missing values that may affect the accuracy of your results. For instance, you might regularly cross-verify data entries to identify and correct any discrepancies.

6. Employ Appropriate Data Analysis Techniques

Select statistical methods that match the nature of your data and research questions. Whether it’s regression analysis, t-tests, ANOVA, or other techniques, using the right approach is important for drawing meaningful conclusions. Utilize software tools like SPSS or R for data analysis to ensure the accuracy and reproducibility of your findings.

7. Interpret Results Objectively

Present your findings clearly and unbiasedly. Avoid making unwarranted causal claims, especially in correlational studies. Instead, focus on describing the relationships and patterns observed in your data.

8. Address Ethical Considerations

Prioritize ethical considerations throughout your research process. Obtain informed consent from participants, ensuring their voluntary participation and confidentiality of data. Comply with ethical guidelines and gain approval from a governing body if necessary.

Enhance Your Quantitative Research With Cutting-Edge Software

While no single research methodology can produce 100% reliable results, you can always opt for a hybrid method by selecting the most relevant methods for your objective.

For the best results, opt for smart, efficient, and scalable research tools that offer delightful reporting and advanced analytics. These tools will make every research initiative a success.

Advanced software tools, such as ProProfs Survey Maker, come with pre-built survey templates and question libraries and allow you to create a high-converting survey in just a few minutes.